Operations | Monitoring | ITSM | DevOps | Cloud

The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.

Hey folks! We are super excited to announce that our schedules feature has gone through a bit of an update. Well, more than a bit 🙂. We’ve gone through the feature with a fine-toothed comb and introduced a bunch of UI and functional improvements which we hope will help you achieve one thing: set up, edit and manage your on-call schedules at scale in a matter of minutes (Yes, that was three things but it was tough to condense it to ONE thing)

The first post in this series introduced the idea of the different layers of value that your business can gain from frequent deployments and focused on the hard costs you can save. We’re looking at the role the database plays here because it’s the most complicated part of the process and it’s difficult to hit aggressive KPIs and goals when your teams are burdened with process bloat due to mistake-prone, manual work.

Toil. Few other words have the same visceral impact for SREs as their four-letter nemesis: toil. Although pretty much everyone recognizes and agrees that toil is bad, it is a term that is frequently misused in colloquial use. In common English usage, toil is defined as “long strenuous fatiguing labor”. As a term of art in the SRE profession, “toil” has several very specific characteristics which distinguish it from other sorts of work which people spend time on.

How can enterprises streamline their operations for improved internal efficiency and customer satisfaction? The right combination of DevOps tools will do just that.

Companies face multiple challenges when migrating their applications and services to the cloud, and one of them is infrastructure management. The ideal scenario would be that all workloads could be containerized. In that case, the organization could use a Kubernetes-based service, like Amazon Web Services (AWS), Google Cloud or Azure, to deploy and manage applications, services and storage in a cloud native environment. Unfortunately, this scenario isn’t always possible.

In a world where everyone across the enterprise requires the network, the Infrastructure & Operations (I&O) team has a lot on their plate. Business units, departments, and even individual employees often need to spin up new network resources in order to do their work, take advantage of new business opportunities, and focus on innovation. As the gatekeepers of the network, it’s up to the I&O team to facilitate these connections.